How to apply O3 and PM2.5 collaborative control to practical management in China: A study based on meta-analysis and machine learning

Liu, Zeyuan; Qi, Zhulin; Ni, Xiufeng; Dong, Mengting; Ma, Mengjie; Xue, Wenhao; Zhang, Qingyu; Wang, Jinnan

doi:10.1016/j.scitotenv.2021.145392

Cited by 39 publications

(22 citation statements)

References 38 publications

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“…For example, Ma et al (Ma et al, 2021a) simulated O3 concentrations in the Beijing-Tianjin-Hebei (BTH) region from 2010-2017 using an RF model that considered meteorological variables and output variables from chemical transport models, and the correlation coefficient (R 2 ) between the observed and modelled O3 concentrations was greater than 0.8. Liu et al (Liu et al, 2021) also reported a high accuracy (80.4%) for classifying pollution levels of O3 and PM2.5 at 1464 monitoring sites in China using an RF model. According to these previous studies, the RF model has shown good performance in terms of prediction accuracy and computational efficiency (Wang et al, 2016;Wang et al, 2017b).…”

Section: Introductionmentioning

confidence: 92%

“…Thus, our model has good prediction performance (R 2 = 0.87) when combined with the initial VOC species. In previous studies using TVOCs, the influence of VOC composition was neglected (Liu et al, 2021;Ma et al, 2021a).…”

Section: Prediction Performance Of the Modelmentioning

confidence: 99%

“…According to these previous studies, the RF model has shown good performance in terms of prediction accuracy and computational efficiency (Wang et al, 2016;Wang et al, 2017b). However, many ML studies have used total VOCs (TVOCs) to simulate O3 formation and rarely considered the effect of VOC species on O3 formation sensitivity (Feng et al, 2019;Liu et al, 2021;Ma et al, 2021a). Thus, they were unable to identify the chemical reactivity of a single species to O3 formation, which may lead to underestimations or even misunderstandings of the role of VOCs in O3 formation because the same concentration of TVOCs with different compositions may lead to different OPEs.…”

Section: Introductionmentioning

confidence: 99%

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Ozone formation sensitivity study using machine learning coupled with reactivity of VOC species

Zhan

Zhang

et al. 2021

Preprint

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Abstract. The formation of ground-level ozone (O3) is dependent on both atmospheric chemical processes and meteorological factors. Traditional models have difficulty assessing O3 formation sensitivity in a timely manner due to the limitations of flexibility and computational efficiency. In this study, a random forest (RF) model coupled with the reactivity of volatile organic compound (VOC) species was used to investigate the O3 formation sensitivity in Beijing from 2014 to 2016, and evaluate the relative importance (RI) of chemical and meteorological factors to O3 formation. The results showed that the O3 prediction performance using initial concentrations of VOC species (R2 = 0.87) was better than that using total VOCs (TVOCs) concentrations (R2 = 0.77). Meanwhile, the RIs of VOC species correlated well with their O3 formation potentials (OFPs). O3 formation presented a negative response to NOx, PM2.5 and relative humidity, and a positive response to temperature, solar radiation and VOCs. The O3 isopleth curves calculated by the RF model were generally comparable with those calculated by the box model. O3 formation shifted from a VOC-limited regime to a transition regime from 2014 to 2016. This study demonstrates that the RF model coupled with the initial concentrations of VOC species could provide an accurate, flexible, and computationally efficient approach for O3 sensitivity analysis.

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Section: Introductionmentioning

confidence: 92%

Section: Prediction Performance Of the Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ozone formation sensitivity study using machine learning coupled with reactivity of VOC species

Zhan

Zhang

et al. 2021

Preprint

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“…Nevertheless, the PM 2.5 -related health impact still could not be ignored (Maji 2020 ; McDuffie et al 2021 ). A collaborative and joint control of PM 2.5 and ozone pollution in China is needed and being discussed (Duan et al 2022 ; Liu et al 2021b , 2021c ; Zhao et al 2021 ). The seasonality of ozone pollution and associated health impacts are more significant in the warm season (Gao et al 2020 ; Lu et al 2020 ), which is relatively opposite of PM 2.5 (Xiao et al 2022 ; Zhao et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Tracking short-term health impacts attributed to ambient PM2.5 and ozone pollution in Chinese cities: an assessment integrates daily population

Guan

Xiao

Zhang

et al. 2022

Environ Sci Pollut Res

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Joint and synergistic control of PM 2.5 and ozone pollution is an urgent need in China and a global-widely concerned issue. Health impact assessment could provide a comprehensive perspective for PM 2.5 -ozone coordinated control strategies. For a detailed understanding of the seasonality and regionality of the health impacts attributed to PM 2.5 and ozone in China, this study extended the classic health impact function by daily population and assessed the short-term (daily) health impacts in 335 Chinese cities in 2021. Population migration indexes from Baidu were introduced to estimate the cities’ daily population. Using this method, we quantitatively investigated the influence of population on short-term health impact assessment and identified which was significant in the Pearl River Delta (PRD) region and other populous cities. Although the annual sums of PM 2.5 - and ozone-related daily health impacts were close for all Chinese cities, the PM 2.5 -related health impact was equivalent to 333.96% and 32.07% of that ozone-related, during the cold and warm periods. The correlation and local spatial association analysis found significant city-specific and city-cluster associations of daily health impacts during the warm period and in Beijing-Tianjin-Hebei and surrounding regions (BTHS) and the Yangtze River Delta (YRD). Policymakers could promote period- and pollutant-targeted control actions for the major city groups, especially the BTHS, YRD, and PRD. Our methods and findings investigated the various influences of the population on short-term health impact assessment and proposed the PM 2.5 -ozone collaborative control idea for key regions and city groups. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-22067-z.

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“…At the same time, nationwide air quality monitoring networks have gradually been established since 2013. Increasing numbers of studies have been conducted to analyze the temporalspatial variations in PM 2.5 based on ground-level monitoring measurements [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Analysis of PM2.5 Variations Based on Observed, Satellite-Derived, and Population-Weighted Concentrations

Fang

Xiong

et al. 2022

Remote Sensing

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Fine particulate matter (PM2.5), which can cause adverse human health effects, has been proven as the first air pollutant in China. In situ observations with ground-level monitoring and satellite-based concentrations have been used to analyze the variations in PM2.5. However, variation analyses based on these two kinds of measurement have mainly focused on the concentration itself and ignored the effects on the population. Therefore, this study not only investigated these two kinds of measurements, but also performed weighted population analyses to study the variations in PM2.5. Firstly, daily models of timely structure adaptive modeling (TSAM) were constructed to simulate satellite-derived PM2.5 levels from January 2013 to December 2016. Secondly, population-weighted concentrations were calculated based on TSAM-derived PM2.5 surfaces. Finally, observed, TSAM-derived, and population-weighted concentrations were used to analyze the variations in PM2.5. The results showed the different importance of various input parameters; AOD had the highest rank. Additionally, TSAM models demonstrated good performance, fitting R ranging from 0.86 to 0.91, and validating R from 0.82 to 0.89. According to the air quality standard in China, TSAM-derived PM2.5 showed that the increase in area lower than Level II was 29.03% and the increase in population was only 14.81%. This indicates that the air quality exhibited an overall improvement in spatial perspective, but some areas with high population density showed a relatively low improvement due to uneven distributions in China. The population-weighted PM2.5 concentration could better represent the health threats of air pollutants compared with in situ observations.

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How to apply O3 and PM2.5 collaborative control to practical management in China: A study based on meta-analysis and machine learning

Cited by 39 publications

References 38 publications

Ozone formation sensitivity study using machine learning coupled with reactivity of VOC species

Ozone formation sensitivity study using machine learning coupled with reactivity of VOC species

Tracking short-term health impacts attributed to ambient PM2.5 and ozone pollution in Chinese cities: an assessment integrates daily population

Analysis of PM2.5 Variations Based on Observed, Satellite-Derived, and Population-Weighted Concentrations

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